Abstract
The monocyte/macrophage lineage has been shown to be involved in the promotion of a protumoral tumor microenvironment and resistance to treatment in B cell lymphomas. However, it is still poorly described at the single cell level, and tissue samples are not easily accessible. Thus, a detailed analysis of the circulating myeloid cell compartment in the different B lymphomas is needed to better understand the mechanisms of resistance to treatment and identify at risk patients. In this Perspective, we review current knowledge on the phenotypic and functional description of the circulating monocytic lineage in B cell lymphomas and provide first insights into the heterogeneity of these cell populations in health and lymphoma, using mass cytometry. Indeed, the monocytic compartment is a continuum more than distinct subpopulations, as demonstrated by our high-resolution approach, explaining the sometimes confusing and contradictory conclusions on the prognostic impact of the different populations, including monocytes and monocytic myeloid derived suppressor cells (M-MDSC). By identifying S100A9high monocytic cells as a potential biomarker in diffuse large B cell lymphoma (DLBCL) in this proof-of-concept preliminary study including a limited number of samples, we underline the potential of circulating myeloid regulatory cells as diagnostic and prognostic biomarkers in B-cell lymphomas.
Introduction
Lymphomas are malignancies that arise from the lymphoid system with an involvement of the B lineage in 90% of cases (1). The lymphoma nomenclature, based on clinical, pathological, genetic, and molecular description (2), is constantly evolving. Different subtypes are described, depending on the stage of maturation from which the tumoral B cell derives. Beside the first historically described Hodgkin lymphoma (HL), non-Hodgkin lymphomas (NHL) include essentially mantle cell lymphoma (MCL), follicular lymphoma (FL), diffuse large B-cell lymphoma (DLBCL), Burkitt lymphoma, and chronic lymphoid leukemia (CLL). As in many cancers, the importance of the tumor microenvironment (TME) in the development of the disease and response to treatment has been emphasized in B-cell lymphoma, in particular, lymphomas can be defined by their dependence to the TME (1, 3–6). The TME is in particular composed of immune cells including T cell subsets (follicular helper T cells [Tfh], regulatory T cells [Treg]), stromal cells, dendritic cells, and myeloid cells (monocytes, macrophages, myeloid derived suppressor cells [MDSC]) and is affected by the tumor localization (e.g. lymph node and bone marrow in FL or blood in CLL). The myeloid cell compartment has lately gained great interest and is extensively studied in several cancer types, including B lymphoma, due to its numerous key and ambivalent roles in pro-tumoral immune suppression, anti-tumor immunity, immunotherapy efficacy through antibody-dependent cellular phagocytosis (ADCP), or as a therapeutic target in immune-checkpoint blockade immunotherapy (1, 7). The monocyte/macrophage lineage has been shown to interact with tumoral and non-tumor B cells in lymphoma, and to be involved in the promotion of protumoral TME and resistance to treatment (8). However, the TME in lymphoma is heterogenous and still poorly described at the single cell level (9, 10). Moreover, there is currently an overall lack of biological material due to the popularity of the fine-needle aspiration for routine diagnosis. Thus, peripheral blood remains of interest for the analysis of immune cells and a fine knowledge of the circulating myeloid cell compartment in the different B lymphomas is needed to better understand the mechanisms of resistance to treatment and identify at risk patients. Herein, we propose to review current advances in the high-resolution description and functional role understanding of circulating monocytic lineage in B cell lymphomas and to discuss future perspectives.
Clinical Relevance of Circulating Myeloid Compartment in B Lymphoma Patients
Myeloid-cell related prognostic signatures have been demonstrated in situ in secondary lymphoid organs of DLBCL, FL, and HL by gene expression profiling studies (4–6), and several studies have shown an association between macrophage infiltration in lymphoma tissues and prognosis (5, 11–15), but the prognostic impact of myeloid cells in the blood is less clear in these pathologies. Nevertheless, soluble prognostic factors related to the biology of myeloid cells have been proposed to be of prognostic interest in these pathologies, including soluble PD-L1 (16, 17), soluble CD163 (18), CXCL10 (19), and IL-10 (19).
More recently, the circulating myeloid compartment, including in particular monocytes and MDSC, has been evaluated as prognostic biomarkers in B lymphoma. An increased monocyte count has been associated with poor prognosis in DLBCL (20–22), HL (23), MCL (24–26), and FL (27). A slightly different approach, with the lymphocyte/monocyte ratio, gives the same results in HL (23, 28, 29) and DLBCL (30). This could be linked to an increase in monocytic myeloid derived suppressor cells (M-MDSC), as it has been suggested in DLBCL (20, 31) and CLL, in which M-MDSC levels correlate with response to treatment (32–35). Concerning the granulocytic lineage, polymorphonuclear MDSC (PMN-MDSC) have been proposed as a marker of poor prognosis in DLBCL (36), but other works did not find any association between PMN-MDSC and DLBCL prognosis (31, 37). In HL, the presence of a CD34pos MDSC subtype in the blood has been associated with worsened prognosis (38).
Overall, there is ample evidence of the clinical relevance of the monocytic lineage, and in particular of circulating suppressor myeloid cells, in the prognosis of patients diagnosed with B lymphomas. However, the existence of conflicting data, largely due to inconsistent phenotype, highlights the need for an in-depth phenotypic study of these populations in order to better characterize them and evaluate their role in these pathologies.
Deciphering the Circulating Myeloid Regulatory Cell Phenotypes in B-cell Lymphomas
To fully understand the role of the monocytic lineage in the physiopathology of B-cell lymphomas, it is first necessary to clearly define these cell subsets. Unfortunately, this raises a pitfall in the different data available on the circulating myeloid compartment in B-cell lymphomas, indeed there is no consensus on the definition of M-MDSC and PMN-MDSC, and monocyte subsets are defined with a continuum of markers (CD14 and CD16). Nevertheless, some low-resolution phenotypical investigations have been performed on these cell subsets in B-cell lymphomas, using various and sometimes overlapping phenotypical definitions.
MDSC is a heterogeneous population of myeloid regulatory cells derived from polymorphonuclear cells (PMN-MDSC) and monocytes (M-MDSC) and defined by their immunosuppressive functions (39, 40). Their existence in inflammatory and cancerous diseases could reshape the TME or more distant sites. Since these cells were first described in mice and defined by their immunosuppressive functions, their identification and study in humans are challenging and discussed. In blood, PMN-MDSC (historically referred to as granulocytic MDSC [G-MDSC]) are classically identified as CD11bpos CD14neg CD15pos or CD11bpos CD14neg CD66bpos, and M-MDSCs as CD11bpos CD14pos HLA-DRneg/low CD15neg. It should be noted that these phenotypical definitions do not discriminate PMN-MDSC from normal neutrophils, as a consequence it is recommended to evaluate PMN-MDSC in the low density fraction after ficoll (39). Linneg HLA-DRneg CD33pos CD123neg cells contain more immature progenitors named early-stage MDSC (e-MDSC) (39–41). Additional markers, including CD116, CD124, VEGF-R, CD11c, CD11b, PD-L1 are commonly used, and various phenotypes have been described in tumors (39, 40, 42).
In human B-cell lymphoma, various PMN-MDSC phenotypes were described. In particular, circulating Linneg/low HLA-DRneg CD11bpos CD33pos cells were increased in HL, FL, DLBCL, and MCL, compared to healthy donors blood (31, 37, 38), whereas CD66bpos CD33dim HLA-DRneg were more abundant in the blood of HL, and indolent or aggressive NHL B-cell lymphomas (36). Finally, immature MDSC defined as CD11bpos CD33pos CD14neg CD34pos HLA‐DRneg were more abundant in HL compared to blood from healthy donors (38). M-MDSC phenotype is more consistent and CD14pos HLA-DRlow/neg cells are accumulated in peripheral blood from FL, DLBCL, MCL, CLL, and HL when compared to healthy samples (20, 31–33, 43, 44). However, HLA-DR is expressed as a continuum and thus the M-MDSC identification remains subjective and will benefit from additional markers and high-resolution analysis.
Overall, comparing all these data is challenging due to the use of different and few phenotypic criteria to assess the myeloid compartment. To overcome these difficulties, high resolution tools, such as mass cytometry, could be of use to fully decipher at the phenotypic level these complex populations in tissue and in blood (45). Deep phenotyping of the circulating myeloid compartment in human blood in both healthy and B-cell lymphoma context is needed. Such data already exist for healthy donor blood, but have not been compared with blood samples obtained from lymphoma patients (9), and a clear landscape of blood mononuclear phagocytes in human health and B-cell lymphoma would allow relevant and reproducible functional experiments.
We propose here such analysis on blood monocytic cells from FL (n=3), DLBCL (n=5), and CLL patients (n=3), compared to healthy donors (n=3) (Table 1), using a previously described mass cytometry (CyTOF) panel dedicated to regulatory myeloid cell exploration (9, 46) (Figure 1). With one exception, all samples were obtained at diagnosis, before any treatment. Our analysis was performed on cryopreserved peripheral blood mononuclear cells (PBMC), thus allowing the exploration of monocytes and M-MDSC, but not PMN-MDSC. Using an unsupervised approach, we realized a dimension reduction followed by clustering, defining 8 monocytic clusters (Figure 1A). The hSNE representation shows all the CD14pos myeloid cells compartment as a continuum, underlining the difficulty to clearly and consistently distinguish different subsets. Nevertheless, based on canonical markers expression, we can identify non-classical CD14dim CD16pos monocytes in cluster 1, classical CD14pos CD16neg HLA-DRpos monocytes in clusters 4, 6, and 8, and putative CD14pos HLA-DRlow M-MDSC in clusters 2, 3, 5, and 7 (Figures 1A, B). Interestingly, the hierarchical clustering of all 8 clusters do not delineate these 3 clusters groups, emphasizing the importance of other less classical markers (Figure 1B). In particular, clusters 2, 3, and 4 display a strong expression of S100A9, a protein which is involved in MDSC metabolism (49). These three clusters are mainly found in DLBCL, the most aggressive disease of our panel, in which they represent up to more than 90% of the monocytic population (Figure 1B). Of note, the cells in cluster 4 highly express HLA-DR, and would usually be included in the classical monocytes subset, even though their phenotype and presence in DLBCL patients may indicate a regulatory M-MDSC-like function. If clusters 1, 5, and 6 are found in all sample types, half of the cells present in cluster 5 come from FL patients. This cluster exhibits an interesting CD14pos HLA-DRlow CD36pos S100A9high M-MDSC like phenotype (Figure 1B). Surprisingly, the cells in cluster 6 display heterogenous and partly low levels of HLA-DR, even though they highly originate from healthy donors. This intriguing result emphasizes the need to not reduce M-MDSC evaluation to HLA-DR expression. Finally, an unsupervised clustering can hide some residual heterogeneity inside the clusters, and thus mask disparities between sample types in a given cluster. For example, the cluster 1 gathers all non-classical monocytes, but looking more precisely at their phenotype across the different sample types, we can see that the cells from DLBCL patients express less HLA-DR and CD36, or that FL cells express more CD32 (Figure 1C). Of course, this preliminary study requires further investigations with a larger number of samples to overcome the limitations due to inter-sample variability, as underlined by the peculiar profiles displayed by one of the HD and one of the CLL samples. It is also difficult to conclude from these findings what impact these phenotypic differences have on cell function, since, for example, the CD32 subtype, which could be an activator or inhibitor, is not known. Linking these phenotypes to cell functionality and pathogeny would therefore require further functional studies.
Table 1
| Disease/sample | Patient ID | Age at diagnosis (years) | Gender (Female/Male) | Cell of origin (DLBCL) or grade (FL) |
|---|---|---|---|---|
| DLBCL | #1 | 76 | F | ABC |
| #2 | 66 | M | GC | |
| #3 | 58 | M | – | |
| #4 | 57 | F | ABC | |
| #5 | 57 | F | ABC | |
| FL | #1 | 65 | M | 3a |
| #2 | 80 | F | 1–2 | |
| #3 | 76 | F | 1–2 | |
| CLL | #1 | 64 | M | – |
| #2 | 84 | M | – | |
| #3 | 57 | F | – | |
| HD | #1 | 50 | M | – |
| #2 | 28 | M | – | |
| #3 | 56 | F | – |
Patients’ characteristics.
DLBCL, diffuse large B cell lymphoma; ABC, activated B-cell; GC, germinal center; cHL, classical Hodgkin lymphoma; FL, follicular lymphoma; CLL, chronic lymphocytic leukemia; HD, healthy donor.
Figure 1
Altogether, these results suggest that circulating monocytic cells display diverse and sometimes specific phenotypes depending on the physiological or lymphomatous context, and that these phenotypes may be related to cell functionality and/or severity of the disease.
Assessing the Myeloid Cells Functional Roles in B-Cell Lymphomas
Even if their definition remains quite elusive, particularly at the phenotypic level, it is now clear that MDSC are important players in B-cell lymphoma pathogenesis. Since the characterization of MDSC, although challenging, is classically based on functional assays (39), we have data on their functional role in tumors, including lymphoma.
In cancer, induction and expansion of MDSC can be induced by factors produced by the tumour or the TME cells (T-cells, macrophages, stromal cells), such as VEGF, GM-CSF, M-CSF, S100A8/9, IL-4, IL-6, or IL-10 (49). MDSC could derive from myeloid progenitors from the bone marrow or the spleen, or arise from monocytes (M-MDSC) or activated mature or immature PMN (PMN-MDSC) (50), after the activation of genes implicated in myeloid differentiation blockade or immune regulation. The main signaling mechanism involved seems to be the phosphorylation of transcription factors known as signal transducer and activator of transcription (STAT) protein family members like STAT3, STAT6, STAT1, STAT5 (40, 51). The fate of these circulating myeloid cells is not completely elucidated. MDSC can differentiate into macrophages and DC (52, 53), especially under hypoxia (54). It has been demonstrated in murine models of solid tumors that MDSC can differentiate into TAM at the tumor site (51, 55–57). MDSC are defined by their immunosuppressive capabilities. They can impair effector T- and NK-cell functions and polarize macrophages into « M2 » phenotype via the expansion of regulatory T cells (Treg), amino acids depletion through arginase-1 (Arg1) and Indoleamine 2,3-dioxygenase (IDO), ROS production through NOX2, expressions of immunoregulatory proteins PDL1, TGFβ, IL-10, and S100A12 (42, 58, 59). The crosstalk between MDSC, macrophages, and DC could enhance the tumor-induced immune suppression (60). MDSC could induce a systemic immune suppression by affecting trafficking of T- and B-cells and reducing antigen response outside tumor sites (61).
Some of these immunosuppressive mechanisms have been demonstrated in B-cell lymphomas. The role of arginine metabolism has been demonstrated in lymphoma mice models (54, 62), where an upregulation of inducible nitric oxide synthase (iNOS) and Arg1 is mediated in MDSC by hypoxia-inducible factor-1a (HIF1a) (54). Another mechanism identified in mice in the upregulation of Arg1 in both PMN-MDSCs and M-MDSC is an increase in the microRNA miR-30a expression, which leads to a decrease in SOCS3 mRNA and the activation of the Janus kinase/STAT3 pathway (63). It has also been demonstrated that MDSC arising from the bone marrow are recruited to the tumor through blood circulation, and that tumor infiltrating M-MDSC mediate the recruitment of Treg in tumor-bearing mice via CCL4 and CCL5 production (64). High levels of ROS were also reported in PMN-MDSCs from the blood of this lymphoma murine model (64). The role of MDSC in IL-10 production is suggested by results in B-cell NHL patients and lymphoma murine models (65), but serum IL-10 could also be produced by lymphoma cells and contribute to an increased number of M-MDSC (66). In DLBCL patients, the release of IL-10, together with S100A12 and an increased PD-L1 expression, could explain the M-MDSC mediated T-cell immunosuppression (31). Accordingly, monocytes from NHL patients (including DLBCL, FL, MCL) induce less T-cell proliferation in vitro than HD monocytes, and the removal of these monocytes restore T cell functions (44). The role of arginine metabolism in the suppressive activity of MDSC from the blood of NHL patients is debated (31, 36, 44), as well as PMN-MDSC pathogeny. Even though no immunosuppressive effect of PMN-MDSC has been reported in a study including only DLBCL patients (31), another study including both HL and B-cell NHL patients showed that in vitro depletion of lymphoma PMN-MDSC restored the proliferation of autologous T cells (36). This could be due to the different phenotypic definition of the cells studied, or to the pathologies included. In CLL, the efficacy of the depletion of monocytes to reduce tumorigenesis has been shown in mice models (67). CLL cells induce IDOhigh MDSC in vitro, and CLL patients display an increase in M-MDSC, with suppressing T cell activity, via IDO and Treg expansion (33). Accordingly, monocytes from CLL patients inhibit T cell proliferation, an effect abrogated by anti-TGFβ, anti-IL10 antibodies and IDO inhibitor, and support Treg expansion (68).
Overall, the sometimes conflicting results of the different works focusing on explaining the functional roles of myeloid cells in B-cell lymphomas highlight the need for a better and clearer definition of these different cells subsets.
Discussion
Over the last few years, an increasing interest has been shown in the myeloid regulatory compartment in B cell lymphomas, and notably the circulating monocytic population. MDSC in particular were shown to be involved in disease prognosis and could have a pathogenic role. However, there is still no consensus on the phenotypic definition of the different circulating myeloid populations. Moreover, the monocytic compartment is more of a continuum than constituted of distinct subtypes, as highlighted by our results, resulting in sometimes contradictory conclusions on the prognostic impact of the different MDSC populations. We confirm here previous results, such as HLA-DR down regulation in B-cell lymphomas, and provide a first exhaustive phenotypic evaluation of the monocytic compartment in different B-cell lymphomas, identifying for example S100A9high monocytic cells as a potential biomarker in DLBCL. This proof-of-concept preliminary study underlines the need to further assess the circulating myeloid compartment at the phenotypic level, on a larger panel of patients and possibly with more markers, to validate clinically relevant prognostic signatures.
The therapeutic management of lymphomas today largely relies on immunotherapy, including rituximab and immune checkpoint blockade. The efficacy of immunotherapy could be modulated by MDSC, as it has been shown in TAM, and their monitoring in the blood could help evaluate response to treatment. However, the characterization of MDSC is currently based on functional assays (39), preventing their monitoring in personalized medicine. Linking easily accessible and monitorable surface phenotypic markers to the functionality of these cells would help overcome this issue. A refined characterization through deep phenotyping of the myeloid compartment could also help identify potential new therapeutic targets (69) and study the immune response to treatment (70).
Altogether, the observations discussed in this article support the idea that circulating myeloid regulatory cells in B-cell lymphomas are promising diagnostic and prognostic biomarkers, and that a comprehensive phenotypic evaluation could serve as a surrogate biomarker of their pathological activity.
Funding
This work was supported by a fellowship from the Nuovo-Soldati Foundation (Switzerland) [M.R.], from the FHU CAMIn (Federation Hospitalo-Universitaire Cancer Microenvironnement et Innovation) [J.F.], from the comité de la recherche clinique et translationnelle (CORECT), CHU of Rennes [F.L.], and from the association pour le développement de l’hématologie oncologie (ADHO) (F.L.).
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Ethics statement
The studies involving human participants were reviewed and approved by IRB Rennes University Hospital. The patients/participants provided their written informed consent to participate in this study.
Author contributions
JF analyzed data and wrote the manuscript; FL, SLG performed experiments; JMI analyzed data; MR designed and supervised research and wrote the manuscript. All authors contributed to the article and approved the submitted version.
Acknowledgments
We are indebted to the clinicians of the BREHAT network and to the French Blood Bank (EFS) of Rennes for providing samples. The authors acknowledge the Centre de Ressources Biologiques (CRB-santé) of Rennes (BB-0033-00056, http://www.crbsante-rennes.com) for managing samples.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
References
1
ScottDWGascoyneRD. The tumour microenvironment in B cell lymphomas. Nat Rev Cancer (2014) 14:517. doi: 10.1038/nrc3774
2
SwerdlowSHCampoEPileriSAHarrisNLSteinHSiebertRet al. The 2016 revision of the World Health Organization classification of lymphoid neoplasms. Blood (2016) 127:2375–90. doi: 10.1182/blood-2016-01-643569
3
Amé-ThomasPTarteK. The yin and the yang of follicular lymphoma cell niches: Role of microenvironment heterogeneity and plasticity. Semin Cancer Biol (2014) 24:23–32. doi: 10.1016/j.semcancer.2013.08.001
4
DaveSSWrightGTanBRosenwaldAGascoyneRDChanWCet al. Prediction of Survival in Follicular Lymphoma Based on Molecular Features of Tumor-Infiltrating Immune Cells. N Engl J Med (2004) 351:2159–69. doi: 10.1056/NEJMoa041869
5
LenzGWrightGDaveSSXiaoWPowellJZhaoHet al. Stromal gene signatures in large-B-cell lymphomas. N Engl J Med (2008) 359:2313–23. doi: 10.1056/NEJMoa0802885
6
SteidlCLeeTShahSPFarinhaPHanGNayarTet al. Tumor-associated macrophages and survival in classic Hodgkin’s lymphoma. N Engl J Med (2010) 362:875–85. doi: 10.1056/NEJMoa0905680
7
NicholasNSApollonioBRamsayAG. Tumor microenvironment (TME)-driven immune suppression in B cell malignancy. Biochim Biophys Acta (2016) 1863:471–82. doi: 10.1016/j.bbamcr.2015.11.003
8
PhamLVPogueEFordRJ. The Role of Macrophage/B-Cell Interactions in the Pathophysiology of B-Cell Lymphomas. Front Oncol (2018) 8:147. doi: 10.3389/fonc.2018.00147
9
RousselMFerrellPBGreenplateARLhommeFGallouSLDigginsKEet al. Mass cytometry deep phenotyping of human mononuclear phagocytes and myeloid-derived suppressor cells from human blood and bone marrow. J Leukoc Biol (2017) 102:437–47. doi: 10.1189/jlb.5MA1116-457R
10
RousselMIrishJMMenardCLhommeFTarteKFestT. Regulatory myeloid cells: an underexplored continent in B-cell lymphomas. Cancer Immunol Immunother (2017) 66:1103–11. doi: 10.1007/s00262-017-2036-5
11
ConnorsJM. Risk assessment in the management of newly diagnosed classical Hodgkin lymphoma. Blood (2015) 125:1693–702. doi: 10.1182/blood-2014-07-537480
12
KeaneCVariFHertzbergMCaoK-ALGreenMRHanEet al. Ratios of T-cell immune effectors and checkpoint molecules as prognostic biomarkers in diffuse large B-cell lymphoma: a population-based study. Lancet Haematol (2015) 2:e445–55. doi: 10.1016/S2352-3026(15)00150-7
13
MarchesiFCirilloMBianchiAGatelyMOlimpieriOMCerchiaraEet al. High density of CD68+/CD163+ tumour-associated macrophages (M2-TAM) at diagnosis is significantly correlated to unfavorable prognostic factors and to poor clinical outcomes in patients with diffuse large B-cell lymphoma. Hematol Oncol (2015) 33:110–2. doi: 10.1002/hon.2142
14
RiihijärviSFiskvikITaskinenMVajavaaraHTikkalaMYriOet al. Prognostic influence of macrophages in patients with diffuse large B-cell lymphoma: a correlative study from a Nordic phase II trial. Haematologica (2015) 100:238–45. doi: 10.3324/haematol.2014.113472
15
VaidyaRWitzigTE. Prognostic factors for diffuse large B-cell lymphoma in the R(X)CHOP era. Ann Oncol Off J Eur Soc Med Oncol (2014) 25:2124–33. doi: 10.1093/annonc/mdu109
16
RossilleDGressierMDamotteDMaucort-BoulchDPangaultCSemanaGet al. High level of soluble programmed cell death ligand 1 in blood impacts overall survival in aggressive diffuse large B-Cell lymphoma: results from a French multicenter clinical trial. Leukemia (2014) 28:2367–75. doi: 10.1038/leu.2014.137
17
RossilleDAzzaouiIFeldmanALMaurerMJLabouréGParrensMet al. Soluble programmed death-ligand 1 as a prognostic biomarker for overall survival in patients with diffuse large B-cell lymphoma: a replication study and combined analysis of 508 patients. Leukemia (2017) 31:988–91. doi: 10.1038/leu.2016.385
18
JonesKVariFKeaneCCrooksPNourseJPSeymourLAet al. Serum CD163 and TARC as disease response biomarkers in classical Hodgkin lymphoma. Clin Cancer Res Off J Am Assoc Cancer Res (2013) 19:731–42. doi: 10.1158/1078-0432.CCR-12-2693
19
CharbonneauBMaurerMJAnsellSMSlagerSLFredericksenZSZiesmerSCet al. Pretreatment circulating serum cytokines associated with follicular and diffuse large B-cell lymphoma: a clinic-based case-control study. Cytokine (2012) 60:882–9. doi: 10.1016/j.cyto.2012.08.028
20
TadmorTFellRPolliackAAttiasD. Absolute monocytosis at diagnosis correlates with survival in diffuse large B-cell lymphoma-possible link with monocytic myeloid-derived suppressor cells. Hematol Oncol (2013) 31:65–71. doi: 10.1002/hon.2019
21
TadmorTBariASacchiSMarcheselliLLiardoEVAviviIet al. Monocyte count at diagnosis is a prognostic parameter in diffuse large B-cell lymphoma: results from a large multicenter study involving 1191 patients in the pre- and post-rituximab era. Haematologica (2014) 99:125–30. doi: 10.3324/haematol.2013.088161
22
WuCWuXLiuXYangPXuJChaiYet al. Prognostic Significance of Monocytes and Monocytic Myeloid-Derived Suppressor Cells in Diffuse Large B-Cell Lymphoma Treated with R-CHOP. Cell Physiol Biochem Int J Exp Cell Physiol Biochem Pharmacol (2016) 39:521–30. doi: 10.1159/000445644
23
TadmorTBariAMarcheselliLSacchiSAvivABaldiniLet al. Absolute Monocyte Count and Lymphocyte-Monocyte Ratio Predict Outcome in Nodular Sclerosis Hodgkin Lymphoma: Evaluation Based on Data From 1450 Patients. Mayo Clin Proc (2015) 90:756–64. doi: 10.1016/j.mayocp.2015.03.025
24
von HohenstaufenKAConconiAde CamposCPFranceschettiSBertoniFMargiotta CasaluciGet al. Prognostic impact of monocyte count at presentation in mantle cell lymphoma. Br J Haematol (2013) 162:465–73. doi: 10.1111/bjh.12409
25
KohYWShinS-JParkCYoonDHSuhCHuhJ. Absolute monocyte count predicts overall survival in mantle cell lymphomas: correlation with tumour-associated macrophages. Hematol Oncol (2014) 32:178–86. doi: 10.1002/hon.2106
26
PorrataLFRistowKMarkovicSN. Absolute monocyte count at diagnosis and survival in mantle cell lymphoma. Br J Haematol (2013) 163:545–7. doi: 10.1111/bjh.12531
27
WilcoxRARistowKHabermannTMInwardsDJMicallefINMJohnstonPBet al. The absolute monocyte count is associated with overall survival in patients newly diagnosed with follicular lymphoma. Leuk Lymphoma (2012) 53:575–80. doi: 10.3109/10428194.2011.637211
28
PorrataLFRistowKColganJPHabermannTMWitzigTEInwardsDJet al. Peripheral blood lymphocyte/monocyte ratio at diagnosis and survival in classical Hodgkin’s lymphoma. Haematologica (2012) 97:262–9. doi: 10.3324/haematol.2011.050138
29
WilcoxRARistowKHabermannTMInwardsDJMicallefINMJohnstonPBet al. The absolute monocyte and lymphocyte prognostic score predicts survival and identifies high-risk patients in diffuse large-B-cell lymphoma. Leukemia (2011) 25:1502–9. doi: 10.1038/leu.2011.112
30
WangJGaoKLeiWDongLXuanQFengMet al. Lymphocyte-to-monocyte ratio is associated with prognosis of diffuse large B-cell lymphoma: correlation with CD163 positive M2 type tumor-associated macrophages, not PD-1 positive tumor-infiltrating lymphocytes. Oncotarget (2017) 8:5414–25. doi: 10.18632/oncotarget.14289
31
AzzaouiIUhelFRossilleDPangaultCDulongJLe PriolJet al. T-cell defect in diffuse large B-cell lymphomas involves expansion of myeloid-derived suppressor cells. Blood (2016) 128:1081–92. doi: 10.1182/blood-2015-08-662783
32
GustafsonMPAbrahamRSLinYWuWGastineauDAZentCSet al. Association of an increased frequency of CD14+ HLA-DR lo/neg monocytes with decreased time to progression in chronic lymphocytic leukaemia (CLL). Br J Haematol (2012) 156:674–6. doi: 10.1111/j.1365-2141.2011.08902.x
33
JitschinRBraunMBüttnerMDettmer-WildeKBricksJBergerJet al. CLL-cells induce IDOhi CD14+HLA-DRlo myeloid-derived suppressor cells that inhibit T-cell responses and promote TRegs. Blood (2014) 124:750–60. doi: 10.1182/blood-2013-12-546416
34
LiuJZhouYHuangQQiuL. CD14+HLA-DRlow/- expression: A novel prognostic factor in chronic lymphocytic leukemia. Oncol Lett (2015) 9:1167–72. doi: 10.3892/ol.2014.2808
35
ZarobkiewiczMKowalskaWChocholskaSTomczakWSzymańskaAMorawskaIet al. High M-MDSC Percentage as a Negative Prognostic Factor in Chronic Lymphocytic Leukaemia. Cancers (2020) 12:2614. doi: 10.3390/cancers12092614
36
MariniOSpinaCMimiolaECassaroAMalerbaGTodeschiniGet al. Identification of granulocytic myeloid-derived suppressor cells (G-MDSCs) in the peripheral blood of Hodgkin and non-Hodgkin lymphoma patients. Oncotarget (2016) 7:27676–88. doi: 10.18632/oncotarget.8507
37
AminiR-MEnbladGHollanderPLaszloSErikssonEAyoola GustafssonKet al. Altered profile of immune regulatory cells in the peripheral blood of lymphoma patients. BMC Cancer (2019) 19:316. doi: 10.1186/s12885-019-5529-0
38
RomanoAParrinelloNLVetroCForteSChiarenzaAFigueraAet al. Circulating myeloid-derived suppressor cells correlate with clinical outcome in Hodgkin Lymphoma patients treated up-front with a risk-adapted strategy. Br J Haematol (2015) 168:689–700. doi: 10.1111/bjh.13198
39
BronteVBrandauSChenS-HColomboMPFreyABGretenTFet al. Recommendations for myeloid-derived suppressor cell nomenclature and characterization standards. Nat Commun (2016) 7:12150. doi: 10.1038/ncomms12150
40
GabrilovichDI. Myeloid-Derived Suppressor Cells. Cancer Immunol Res (2017) 5:3–8. doi: 10.1158/2326-6066.CIR-16-0297
41
UhelFAzzaouiILe GallouSFestTTarteKRousselM. Early-stage myeloid-derived suppressor cell count: Basophil exclusion matters. J Allergy Clin Immunol (2019) 144:1125–7. doi: 10.1016/j.jaci.2019.06.027
42
ParkerKHBeuryDWOstrand-RosenbergS. Myeloid-Derived Suppressor Cells: Critical Cells Driving Immune Suppression in the Tumor Microenvironment. Adv Cancer Res (2015) 128:95–139. doi: 10.1016/bs.acr.2015.04.002
43
GustafsonMPLinYMaasMLVan KeulenVPJohnstonPBPeikertTet al. A method for identification and analysis of non-overlapping myeloid immunophenotypes in humans. PloS One (2015) 10:e0121546. doi: 10.1371/journal.pone.0121546
44
LinYGustafsonMPBulurPAGastineauDAWitzigTEDietzAB. Immunosuppressive CD14+HLA-DR(low)/- monocytes in B-cell non-Hodgkin lymphoma. Blood (2011) 117:872–81. doi: 10.1182/blood-2010-05-283820
45
FerrantJLe GallouSMansonGGenebrierSMourcinFTarteKRousselM. High-Dimensional Phenotyping of Human Myeloid-Derived Suppressor Cells/Tumor-Associated Macrophages in Tissue by Mass Cytometry. Methods Mol Biol (2021) 2236:57–66. doi: 10.1007/978-1-0716-1060-2
46
RousselMLhommeFRoeCEBartkowiakTGravellePLaurentCet al. Mass cytometry defines distinct immune profile in germinal center B-cell lymphomas. Cancer Immunol Immunother CII (2020) 69:407–20. doi: 10.1007/s00262-019-02464-z
47
HölltTPezzottiNvanUVKoningFEisemannELelieveldtBet al. Cytosplore: Interactive Immune Cell Phenotyping for Large Single-Cell Datasets. Comput Graph Forum (2016) 35:171–80. doi: 10.1111/cgf.12893
48
van UnenVHölltTPezzottiNLiNReindersMJTEisemannEet al. Visual analysis of mass cytometry data by hierarchical stochastic neighbour embedding reveals rare cell types. Nat Commun (2017) 8:1740. doi: 10.1038/s41467-017-01689-9
49
GabrilovichDINagarajS. Myeloid-derived suppressor cells as regulators of the immune system. Nat Rev Immunol (2009) 9:162–74. doi: 10.1038/nri2506
50
BizymiNBjelicaSKittangAOMojsilovicSVelegrakiMPontikoglouCet al. Myeloid-Derived Suppressor Cells in Hematologic Diseases: Promising Biomarkers and Treatment Targets. HemaSphere (2019) 3:e168. doi: 10.1097/HS9.0000000000000168
51
KumarVChengPCondamineTMonySLanguinoLRMcCaffreyJCet al. CD45 Phosphatase Inhibits STAT3 Transcription Factor Activity in Myeloid Cells and Promotes Tumor-Associated Macrophage Differentiation. Immunity (2016) 44:303–15. doi: 10.1016/j.immuni.2016.01.014
52
KusmartsevSGabrilovichDI. Inhibition of myeloid cell differentiation in cancer: the role of reactive oxygen species. J Leukoc Biol (2003) 74:186–96. doi: 10.1189/jlb.0103010
53
KusmartsevSGabrilovichDI. STAT1 signaling regulates tumor-associated macrophage-mediated T cell deletion. J Immunol Baltim Md 1950 (2005) 174:4880–91. doi: 10.4049/jimmunol.174.8.4880
54
CorzoCACondamineTLuLCotterMJYounJ-IChengPet al. HIF-1α regulates function and differentiation of myeloid-derived suppressor cells in the tumor microenvironment. J Exp Med (2010) 207:2439–53. doi: 10.1084/jem.20100587
55
KumarVPatelSTcyganovEGabrilovichDI. The Nature of Myeloid-Derived Suppressor Cells in the Tumor Microenvironment. Trends Immunol (2016) 37:208–20. doi: 10.1016/j.it.2016.01.004
56
MantovaniAMarchesiFMalesciALaghiLAllavenaP. Tumour-associated macrophages as treatment targets in oncology. Nat Rev Clin Oncol (2017) 14:399. doi: 10.1038/nrclinonc.2016.217
57
UgelSDe SanctisFMandruzzatoSBronteV. Tumor-induced myeloid deviation: when myeloid-derived suppressor cells meet tumor-associated macrophages. J Clin Invest (2015) 125:3365–76. doi: 10.1172/JCI80006
58
GabrilovichDIOstrand-RosenbergSBronteV. Coordinated regulation of myeloid cells by tumours. Nat Rev Immunol (2012) 12:253–68. doi: 10.1038/nri3175
59
NagarajSSchrumAGChoH-ICelisEGabrilovichDI. Mechanism of T cell tolerance induced by myeloid-derived suppressor cells. J Immunol Baltim Md 1950 (2010) 184:3106–16. doi: 10.4049/jimmunol.0902661
60
Ostrand-RosenbergSSinhaPBeuryDWClementsVK. Cross-talk between myeloid-derived suppressor cells (MDSC), macrophages, and dendritic cells enhances tumor-induced immune suppression. Semin Cancer Biol (2012) 22:275–81. doi: 10.1016/j.semcancer.2012.01.011
61
KuAWMuhitchJBPowersCADiehlMKimMFisherDTet al. Tumor-induced MDSC act via remote control to inhibit L-selectin-dependent adaptive immunity in lymph nodes. eLife (2016) 5:e17375. doi: 10.7554/eLife.17375
62
SerafiniPMgebroffSNoonanKBorrelloI. Myeloid-derived suppressor cells promote cross-tolerance in B-cell lymphoma by expanding regulatory T cells. Cancer Res (2008) 68:5439–49. doi: 10.1158/0008-5472.CAN-07-6621
63
XuZJiJXuJLiDShiGLiuFet al. MiR-30a increases MDSC differentiation and immunosuppressive function by targeting SOCS3 in mice with B-cell lymphoma. FEBS J (2017) 284:2410–24. doi: 10.1111/febs.14133
64
SchleckerEStojanovicAEisenCQuackCFalkCSUmanskyVet al. Tumor-infiltrating monocytic myeloid-derived suppressor cells mediate CCR5-dependent recruitment of regulatory T cells favoring tumor growth. J Immunol Baltim Md 1950 (2012) 189:5602–11. doi: 10.4049/jimmunol.1201018
65
SatoYShimizuKShingaJHidakaMKawanoFKakimiKet al. Characterization of the myeloid-derived suppressor cell subset regulated by NK cells in malignant lymphoma. Oncoimmunology (2015) 4:e995541. doi: 10.1080/2162402X.2014.995541
66
XiuBLinYGroteDMZiesmerSCGustafsonMPMaasMLet al. IL-10 induces the development of immunosuppressive CD14(+)HLA-DR(low/-) monocytes in B-cell non-Hodgkin lymphoma. Blood Cancer J (2015) 5:e328. doi: 10.1038/bcj.2015.56
67
HannaBSMcClanahanFYazdanparastHZaborskyNKalterVRößnerPMet al. Depletion of CLL-associated patrolling monocytes and macrophages controls disease development and repairs immune dysfunction in vivo. Leukemia (2016) 30:570–9. doi: 10.1038/leu.2015.305
68
GiannoniPPietraGTravainiGQuartoRShytiGBenelliRet al. Chronic lymphocytic leukemia nurse-like cells express hepatocyte growth factor receptor (c-MET) and indoleamine 2,3-dioxygenase and display features of immunosuppressive type 2 skewed macrophages. Haematologica (2014) 99:1078–87. doi: 10.3324/haematol.2013.091405
69
EngblomCPfirschkeCPittetMJ. The role of myeloid cells in cancer therapies. Nat Rev Cancer (2016) 16:447–62. doi: 10.1038/nrc.2016.54
70
BendallSCSimondsEFQiuPAmir E -a. DKrutzikPOFinckRet al. Single-Cell Mass Cytometry of Differential Immune and Drug Responses Across a Human Hematopoietic Continuum. Science (2011) 332:687–96. doi: 10.1126/science.1198704
Summary
Keywords
monocyte, myeloid-derived suppressor cells, lymphoma, biomarker, mass cytometry
Citation
Ferrant J, Lhomme F, Le Gallou S, Irish JM and Roussel M (2021) Circulating Myeloid Regulatory Cells: Promising Biomarkers in B-Cell Lymphomas. Front. Immunol. 11:623993. doi: 10.3389/fimmu.2020.623993
Received
30 October 2020
Accepted
14 December 2020
Published
02 February 2021
Volume
11 - 2020
Edited by
Martina Seiffert, German Cancer Research Center (DKFZ), Germany
Reviewed by
Selcen Öztürk, German Cancer Research Center (DKFZ), Germany; Tiziana Schioppa, University of Brescia, Italy
Updates
Copyright
© 2021 Ferrant, Lhomme, Le Gallou, Irish and Roussel.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Mikael Roussel, mikael.roussel@chu-rennes.fr
This article was submitted to Cancer Immunity and Immunotherapy, a section of the journal Frontiers in Immunology
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